Use of aromatic plants and essential oils in the teaching of physics and chemistry to enhance motivation and sustainability awareness among primary education trainee teachers.

This work faces the known problem of negative visión among prospective primary teachers regarding science teaching and learning. This is even more acute if gender is taken into account. To overcome this, the use of ethnobotany as a science of proximity is proposed. Specifically, an educational intervention is described that is based on a combination of active methodologies and the use of ethnobotany as a teaching tool for a subject with physics and chemistry contents. Ninety-two university-based primary education trainee teachers participated in the research. By means of questionnaires, information was collected regarding attitudes toward the subject and the use of ethnobotany as a teaching tool, evaluation of the proposal, sustainability awareness, and affective and emotional dimensions. The data indicated a great impact and acceptability. The results show a significant increase in motivation, an improvement in attitudes toward sustainable behavior and a favorable assessment of the proposed didactic tool. This hybrid methodology demonstrates, in the context used, effectiveness in improving the perception of science.

The Evolution of Active Learning in Response to the Pandemic: The Role of Technology.

Active learning has consistently played a significant role in education. Through interactive tasks, group projects, and a variety of engaging activities, students are encouraged to forge connections with the subject matter. However, the pandemic has necessitated that educators adapt and refine their active learning techniques to accommodate the online environment. This has resulted in stimulating innovations in the field, encompassing virtual simulations, online collaboration tools, and interactive multimedia. The COVID-19 pandemic has rapidly transformed the landscape of teaching and learning, particularly in higher education. One of the most prominent shifts has been the widespread adoption of active learning techniques, which have increased student engagement and fostered deeper learning experiences. In this chapter, we examine the evolution of active learning during the pandemic, emphasizing its advantages and challenges. Furthermore, we delve into the role of advances in artificial intelligence and their potential to enhance the effectiveness of active learning approaches. As we once focused on leveraging the opportunities of remote teaching, we must now shift our attention to harnessing the power of AI responsibly and ethically to benefit our students. Drawing from our expertise in educational innovation, we provide insights and recommendations for educators aiming to maximize the benefits of active learning in the post-pandemic era.

Enhancing Buoyant force learning through a visuo-haptic environment: a case study.

Introduction: This study aimed to develop, implement, and test a visuo-haptic simulator designed to explore the buoyancy phenomenon for freshman engineering students enrolled in physics courses. The primary goal was to enhance students' understanding of physical concepts through an immersive learning tool. Methods: The visuo-haptic simulator was created using the VIS-HAPT methodology, which provides high-quality visualization and reduces development time. A total of 182 undergraduate students were randomly assigned to either an experimental group that used the simulator or a control group that received an equivalent learning experience in terms of duration and content. Data were collected through pre- and post-tests and an exit-perception questionnaire. Results: Data analysis revealed that the experimental group achieved higher learning gains than the control group (p = 0.079). Additionally, students in the experimental group expressed strong enthusiasm for the simulator, noting its positive impact on their understanding of physical concepts. The VIS-HAPT methodology also reduced the average development time compared to similar visuo-haptic simulators. Discussion: The results demonstrate the efficacy of the buoyancy visuo-haptic simulator in improving students' learning experiences and validate the utility of the VIS-HAPT method for creating immersive educational tools in physics.

Exploring the Loss Aversion Scale's psychometric properties in Spain.

Loss aversion is a psychological construct defined as a tendency to value potential losses more than gains in a situation that requires decision-making. The Loss Aversion Scale (LAS, eight items) measures an individual's loss aversion to various situations. However, the generalization of its psychometric properties to different population groups is unknown. This study aimed to validate the LAS instrument for use among Spanish university adults. To this end, two studies were conducted: a content validity study calculating the substantive validity (N = 24) of the instrument's translation from original English to Spanish and a study of internal structure and association (N = 766) among Spanish university men and women aged 18-35. The analyses performed for each sample indicated that the instrument had adequate validity and reliability values as a one-dimensional measure; however, items 5 and 8 had to be removed. Their scores indicated moderate-magnitude correlations with social desirability. This article debates the study's limitations, practical implications, and future lines of research based on the results. The conclusion is that the Loss Aversion Scale instrument suits general Spanish population samples and requires probable methodological control concerning social desirability.

Complex artificial intelligence models for energy sustainability in educational buildings.

Energy consumption of constructed educational facilities significantly impacts economic, social and environment sustainable development. It contributes to approximately 37% of the carbon dioxide emissions associated with energy use and procedures. This paper aims to introduce a study that investigates several artificial intelligence-based models to predict the energy consumption of the most important educational buildings; schools. These models include decision trees, K-nearest neighbors, gradient boosting, and long-term memory networks. The research also investigates the relationship between the input parameters and the yearly energy usage of educational buildings. It has been discovered that the school sizes and AC capacities are the most impact variable associated with higher energy consumption. While 'Type of School' is less direct or weaker correlation with 'Annual Consumption'. The four developed models were evaluated and compared in training and testing stages. The Decision Tree model demonstrates strong performance on the training data with an average prediction error of about 3.58%. The K-Nearest Neighbors model has significantly higher errors, with RMSE on training data as high as 38,429.4, which may be indicative of overfitting. In contrast, Gradient Boosting can almost perfectly predict the variations within the training dataset. The performance metrics suggest that some models manage this variability better than others, with Gradient Boosting and LSTM standing out in terms of their ability to handle diverse data ranges, from the minimum consumption of approximately 99,274.95 to the maximum of 683,191.8. This research underscores the importance of sustainable educational buildings not only as physical learning spaces but also as dynamic environments that contribute to informal educational processes. Sustainable buildings serve as real-world examples of environmental stewardship, teaching students about energy efficiency and sustainability through their design and operation. By incorporating advanced AI-driven tools to optimize energy consumption, educational facilities can become interactive learning hubs that encourage students to engage with concepts of sustainability in their everyday surroundings.

Learning manufacturing computer vision systems using tiny YOLOv4.

Implementing and deploying advanced technologies are principal in improving manufacturing processes, signifying a transformative stride in the industrial sector. Computer vision plays a crucial innovation role during this technological advancement, demonstrating broad applicability and profound impact across various industrial operations. This pivotal technology is not merely an additive enhancement but a revolutionary approach that redefines quality control, automation, and operational efficiency parameters in manufacturing landscapes. By integrating computer vision, industries are positioned to optimize their current processes significantly and spearhead innovations that could set new standards for future industrial endeavors. However, the integration of computer vision in these contexts necessitates comprehensive training programs for operators, given this advanced system's complexity and abstract nature. Historically, training modalities have grappled with the complexities of understanding concepts as advanced as computer vision. Despite these challenges, computer vision has recently surged to the forefront across various disciplines, attributed to its versatility and superior performance, often matching or exceeding the capabilities of other established technologies. Nonetheless, there is a noticeable knowledge gap among students, particularly in comprehending the application of Artificial Intelligence (AI) within Computer Vision. This disconnect underscores the need for an educational paradigm transcending traditional theoretical instruction. Cultivating a more practical understanding of the symbiotic relationship between AI and computer vision is essential. To address this, the current work proposes a project-based instructional approach to bridge the educational divide. This methodology will enable students to engage directly with the practical aspects of computer vision applications within AI. By guiding students through a hands-on project, they will learn how to effectively utilize a dataset, train an object detection model, and implement it within a microcomputer infrastructure. This immersive experience is intended to bolster theoretical knowledge and provide a practical understanding of deploying AI techniques within computer vision. The main goal is to equip students with a robust skill set that translates into practical acumen, preparing a competent workforce to navigate and innovate in the complex landscape of Industry 4.0. This approach emphasizes the criticality of adapting educational strategies to meet the evolving demands of advanced technological infrastructures. It ensures that emerging professionals are adept at harnessing the potential of transformative tools like computer vision in industrial settings.

Implementing a challenge-based learning experience in a bioinstrumentation blended course.

BACKGROUND: Bioinstrumentation is essential to biomedical engineering (BME) undergraduate education and professional practice. Several strategies have been suggested to provide BME students with hands-on experiences throughout the curriculum, promoting their preparedness to pursue careers in industry and academia while increasing their learning and engagement. This paper describes the implementation of challenge-based learning (CBL) in an undergraduate bioinstrumentation blended course over the COVID-19 pandemic. METHODS: The CBL experience was implemented in a third-year bioinstrumentation course from the BME program at Tecnologico de Monterrey. Thirty-nine students enrolled in two sections formed fourteen teams that tackled blended learning activities, including online communication, lab experiments, and in-person CBL activities. Regarding the latter, students were challenged to design, prototype, and test a respiratory or cardiac gating device for radiotherapy. An institutional student opinion survey was used to assess the success of our CBL implementation. RESULTS: Student responses to the end-of-term survey showed that they strongly agreed that this course challenged them to learn new concepts and develop new skills. Furthermore, they rated the student-lecturer interaction very positively despite the blended format. Overall, students assessed their learning experience positively. However, implementing this CBL experience required a substantial time increase in planning, student tutoring, and constant communication between lecturers and the industry partner. CONCLUSION: This work provides an effective instance of CBL for BME education to improve students' learning experience despite decreased resource efficiency. Our claim is supported by the student's performance and the positive feedback from our industrial partner.

Sorption Behavior of Azo Dye Congo Red onto Activated Biochar from Haematoxylum campechianum Waste: Gradient Boosting Machine Learning-Assisted Bayesian Optimization for Improved Adsorption Process.

This work aimed to describe the adsorption behavior of Congo red (CR) onto activated biochar material prepared from Haematoxylum campechianum waste (ABHC). The carbon precursor was soaked with phosphoric acid, followed by pyrolysis to convert the precursor into activated biochar. The surface morphology of the adsorbent (before and after dye adsorption) was characterized by scanning electron microscopy (SEM/EDS), BET method, X-ray powder diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR) and, lastly, pHpzc was also determined. Batch studies were carried out in the following intervals of pH = 4-10, temperature = 300.15-330.15 K, the dose of adsorbent = 1-10 g/L, and isotherms evaluated the adsorption process to determine the maximum adsorption capacity (Qmax, mg/g). Kinetic studies were performed starting from two different initial concentrations (25 and 50 mg/L) and at a maximum contact time of 48 h. The reusability potential of activated biochar was evaluated by adsorption-desorption cycles. The maximum adsorption capacity obtained with the Langmuir adsorption isotherm model was 114.8 mg/g at 300.15 K, pH = 5.4, and a dose of activated biochar of 1.0 g/L. This study also highlights the application of advanced machine learning techniques to optimize a chemical removal process. Leveraging a comprehensive dataset, a Gradient Boosting regression model was developed and fine-tuned using Bayesian optimization within a Python programming environment. The optimization algorithm efficiently navigated the input space to maximize the removal percentage, resulting in a predicted efficiency of approximately 90.47% under optimal conditions. These findings offer promising insights for enhancing efficiency in similar removal processes, showcasing the potential of machine learning in process optimization and environmental remediation.

Improving engineering students' understanding of classical physics through visuo-haptic simulations.

Introduction: The teaching process plays a crucial role in the training of professionals. Traditional classroom-based teaching methods, while foundational, often struggle to effectively motivate students. The integration of interactive learning experiences, such as visuo-haptic simulators, presents an opportunity to enhance both student engagement and comprehension. Methods: In this study, three simulators were developed to explore the impact of visuo-haptic simulations on engineering students' engagement and their perceptions of learning basic physics concepts. The study used an adapted end-user computing satisfaction questionnaire to assess students' experiences and perceptions of the simulators' usability and its utility in learning. Results: Feedback from participants suggests a positive reception towards the use of visuo-haptic simulators, highlighting their usefulness in improving the understanding of complex physics principles. Discussion: Results suggest that incorporating visuo-haptic simulations into educational contexts may offer significant benefits, particularly in STEM courses, where traditional methods may be limited. The positive responses from participants underscore the potential of computer simulations to innovate pedagogical strategies. Future research will focus on assessing the effectiveness of these simulators in enhancing students' learning and understanding of these concepts in higher-education physics courses.

Computational thinking and programming with Arduino in education: A systematic review for secondary education.

The development of programming skills and computational thinking in the formal educational context is one of the most recent horizons set by many educational systems worldwide. Although the first computational thinking initiatives are being applied from the earliest school ages, this research focuses on the secondary education level. Specifically, the objective is the following: to analyse the implementation of Arduino, as well as the benefits and opportunities it brings to secondary school students. For this purpose, documentary research has been undertaken applying a systematic review according to the PRISMA 2020 framework following the PiCoS strategy. Atlas.ti 9 was used to analyse the information. Out of 316 papers identified, 37 were included in the research. In relation to the results, Arduino is primarily used in technology and physics subjects, although it is also used to develop interdisciplinary STEAM projects. As a rule, it is used to learn programming languages, but likewise as a resource to develop science experiments. LED lights, servomotors and breadboards are among the most commonly used resources together with the Arduino board. and Scratch was the most widely used software. The initiatives implemented have yielded both positive and negative results, for example, one drawback is that some projects are very difficult, and some achievements such as: increased motivation towards the contents addressed or also the development of some soft skills, such as problem solving.

Catering for the Needs of Diverse Patient Populations: Using ChatGPT to Design Case-Based Learning Scenarios.

Artificial intelligence (AI) represents an opportunity for medical education to enhance efficiency, interactivity, and realism in learning scenarios. This project uses it to identify angles we have not considered before, particularly in creating culturally sensitive educational cases that represent the needs of a diverse patient population. The implementation showed encouraging results, as the ChatGPT algorithm was successful in writing cases that are more culturally sensitive; however, iteration for refinement was needed. An evolution of these prompts and resulting cases are presented. AI-generated material is only as good as the prompts we use, and how we define the task depends on digital literacy and pedagogical intent.

Artificial intelligence, applications and challenges in simulation-based education

The rapid advancement of Artificial Intelligence (AI) has taken the world by "surprise" due to the lack of regulation over this technological innovation which, while promising application opportunities in different fields of knowledge, including education, simultaneously generates concern, rejection and even fear. In the field of Health Sciences Education, clinical simulation has transformed educational practice; however, its formal insertion is still heterogeneous, and we are now facing a new technological revolution where AI has the potential to transform the way we conceive its application.

El rápido avance de la inteligencia artificial (IA) ha tomado al mundo por "sorpresa" debido a la falta de regulación sobre esta innovación tecnológica, que si bien promete oportunidades de aplicación en diferentes campos del conocimiento, incluido el educativo, también genera preocupación e incluso miedo y rechazo. En el campo de la Educación en Ciencias de la Salud la Simulación Clínica ha transformado la práctica educativa; sin embargo, aún es heterogénea su inserción formal, y ahora nos enfrentamos a una nueva revolución tecnológica, en la que las IA tienen el potencial de transformar la manera en que concebimos su aplicación.

Positive mental health of Latin American university professors: A scientific framework for intervention and improvement.

The post-pandemic stage covid-19 has revealed overloads, ambiguities, and conflicts of teachers in the performance of new roles in hybrid classrooms that demanded an urgent adaptation, this highlighted the need for priority attention to the mental health of teachers, however, there are still insufficient studies that transcend the diagnosis and are committed to establish proposals for improvement. OBJECTIVE: This study aims to establish a proposal for the promotion of positive mental health (PMH). METHODS: The study was deployed from a qualitative approach; using an ethnomethodological design that allowed studying how teachers create meanings and sense in their work context, an appreciative interview was conducted with an affirmative theme that allowed teachers to expose their experiences that were systematized and processed with ATLAS. ti software. The application of the interview was conducted online through a Google form, during the months of February and March 2023. Three hundred university professors who experienced the pandemic in universities in Brazil, Chile, Colombia, Ecuador, Mexico, and Peru participated, based on a convenience sampling. RESULTS: The results of the deductive phase confirmed Lluch's PMH theoretical framework; however, new nuances or variations have been identified, which must be considered in the complex and dynamic nature of each PMH factor. From there, the results of the inductive phase allowed revealing emerging concepts, that is, new categories that would have the function of improving the PMH factors, which is why they have been denominated: dynamizing nuclei. PMH dynamizing nuclei are adjustment to work environment, soft skills, work-family balance, self-motivation, self-efficacy, subjective well-being, proactive strategies, engagement, resilience. CONCLUSIONS: Finally, with the results of both phases, the creation of an integrated model was generated, which was evaluated by six experts in a round of feedback, who highlighted the relevance of the findings and offered recommendations that were considered in the study. The new integrated model has revealed an interesting association, since it not only legitimizes the PMH's dynamizing cores, but also informs on which specific factor of the PMH these cores have the greatest impact, which has a high guiding value for intervention and improvement based on focused strategies.

New challenges in mitigating climate change: Digital teaching for the sustainable development and innovation.

The phenomenon of climate change has become a global challenge that affects human activities in many ways. Exploring the sustainability and innovativeness of digital education is an important reference for the further implementation of science and education strategies and positive effects on climate change mitigation. This study designed two questionnaires for basic and tertiary education to address variability in education and collected 523 samples for basic education and 412 samples for tertiary education respectively. Using digital teaching practices and digital teaching quality as mediators, structural equation modelling (SEM) was used to examine the impact of digital education on sustainability and innovation in education. Research shows that digital teaching and learning in basic and higher education can significantly contribute to the two-way development of educational sustainability and innovation, through digital teaching and learning practices. Digital teaching practice and digital teaching quality in higher education play a complete mediating role, while basic education only plays a partial mediating role. Higher education emphasizes digital teaching and learning practice processes and teaching quality, and most students hope that online teaching and learning can be further integrated with offline education to form a new model of education. This study will help government departments understand the pedagogical reality of digital education at a deeper level and provide ideas for the subsequent sustainable development and educational innovation of digital teaching. It can also provide new ideas for climate change mitigation and sustainable development.

Proyección didáctica educativa mediante la plataforma Moodle en la disciplina Bases Biológicas de la Medicina

Introducción: Las decisiones tomadas para la conducción adecuada de los procesos académicos comprometen la responsabilidad ante la formación profesional. Objetivo: Analizar la proyección didáctica educativa asumida por el Departamento de Ciencias Básicas Biomédicas, de la Facultad de Ciencias Médicas Salvador Allende, con el uso de la plataforma Moodle en la enseñanza-aprendizaje de la Disciplina Bases Biológicas de la Medicina. Posicionamiento de los autores: Se constató que se ha venido haciendo uso de la plataforma Moodle como un recurso para implementar aulas virtuales complementarias a la docencia presencial, en las que se promueve la participación consciente y activa de los estudiantes en el proceso de su formación, cuyo funcionamiento se propone mejorar. Conclusiones: El análisis sobre las decisiones tomadas por el Departamento de Ciencias Básicas Biomédicas, de la Facultad de Ciencias Médicas Salvador Allende, con respecto al uso docente de la plataforma Moodle, permitió establecer que dichas determinaciones resultan adecuadas, al coincidir con las normas generales que suponen una gestión didáctica pertinente, característica que contribuye al cumplimiento de uno de los indicadores de la responsabilidad social universitaria. Se sugiere promover institucionalmente esta posición acerca del trabajo metodológico en otros departamentos docentes que solamente han desarrollado repositorios de información.

Introduction: Decisions made for conducting academic processes properly compromise the responsibility to respond for professional training. Objective: To analyze the educational-didactic projection assumed by the Department of Basic Biomedical Sciences of Facultad de Ciencias Médicas Salvador Allende with respect to the use of the Moodle platform in the teaching-learning of the discipline Biological Basis of Medicine. Authors' Position: The Moodle platform was found to have been used as a resource to implement virtual classrooms complementary to face-to-face teaching, where promotion is made for the conscious and active participation of students in the process of their training, and whose operation has been suggested to be improved. Conclusions: The analysis on the decisions made by the Department of Basic Biomedical Sciences at Facultad de Ciencias Médicas Salvador Allende with respect to the teaching use of the Moodle platform allowed establishing that such determinations are adequate, as they coincide with the general standards based on a relevant didactic management, a characteristic that contributes to the fulfillment of one of the indicators of university social responsibility. This position towards methodological work is suggested to be promoted institutionally in other teaching departments that have only developed information repositories.

Predicting open education competency level: A machine learning approach.

This article aims to study open education competency data through machine learning models to determine whether models can be built on decision rules using the features from the students' perceptions and classify them by the level of competency. Data was collected from a convenience sample of 326 students from 26 countries using the eOpen instrument. Based on a quantitative research approach, we analyzed the eOpen data using two machine learning models considering these findings: 1) derivation of decision rules from students' perceptions of knowledge, skills, and attitudes or values related to open education to predict their competence level using Decision Trees and Random Forests models, 2) analysis of the prediction errors in the machine learning models to find bias, and 3) description of decision trees from the machine learning models to understand the choices that both models made to predict the competency levels. The results confirmed our hypothesis that the students' perceptions of their knowledge, skills, and attitudes or values related to open education and its sub-competencies produced satisfactory data for building machine learning models to predict the participants' competency levels.

Evaluation of online learning readiness in the new distance learning normality.

The COVID-19 pandemic influenced teaching and learning in higher education. The transformation towards digital education challenged Faculty and students. This research examines the online learning readiness of students in a Higher Education Institution in Mexico. Specifically, we investigated how much prior digital skills, as well as having used the digital resources available by the university, influenced their academic achievement in distance learning settings. Seven dimensions of online learning readiness were selected to evaluate the student's preparation for the online learning process. Questionnaires were applied before the start and at the end of digital courses. Follow-up tools were offered to support the student, and two groups were observed, users and non-users of the digital devices. It was observed that students who used the support developed significantly better critical thinking, problem-solving, and time organization skills than non-users. On the other hand, although the evaluations were not significantly different, the lowest averages were found in the non-user group. Our results indicate that prior training in the use of digital tools is essential for the success of online education; in the same way, a timely follow-up with technical and pedagogical assistance is necessary for developing competencies. Training more autonomous and independent students capable of distance learning in a global world demands experts in digital education urgently. Educational institutions must embrace new technologies and teaching methods to meet the ever-changing needs of students. This research is expected to play a crucial role in promoting constructive discussions and facilitating informed decisions concerning the creation of future educational models.

Competencies Development Strategy Using Augmented Reality for Self-Management of Learning in Manufacturing Laboratories (AR-ManufacturingLab).

Technological tools in education open new learning possibilities. This proposal describes integrating augmented reality and different educational elements to develop engineering competencies, specifically using specialized machinery in the manufacturing laboratory. It is necessary to encourage self-management of learning, where users interact with the devices employing an application that contains significant information about procedures to perform and essential elements to consider when manipulating the machinery. The machines involved include industrial robots, CNC (Computer Numerical Control) lathes and milling machines, and PLCs (Programmable Logic Controls). In a traditional training model, an instructor guides the users in learning how to manipulate the equipment. This proposal intends that the participants use their mobile devices to receive each machine's instruction and the necessary documentation. The participants' learning pace varies. So, the research aimed to reduce the deficit in the disciplinary competencies since, in the traditional methodology, there is no heterogeneity in understanding the contents. We designed a competency development strategy with six laboratory practices, integrating multimedia elements to address the significant learning content of each manufacturing cell device. Each lesson contained information about each device and a practice activity and self-evaluation to ensure learning the content.

Maisotsenko Cycle for Heat Recovery in Gas Turbines: A Fundamental Thermodynamic Assessment.

This paper reports the Maisotsenko's cycle-based waste heat recovery system with enhanced humidification to exploit the maximum waste heat recovery potential of the gas turbine. This research uses an integrated methodology coupling thermodynamic balances with heat transfer model of air saturator. The performance of the system is deduced which are assisted with sensitivity analysis indicating the optimal mass flow rate ratio (0.7-0.8) and pressure ratio (4.5-5.0) between the topping and bottoming cycles, and the air saturator split (extraction) ratio (0.5). The net-work output, energy, and exergy efficiencies of the system are found to be ≈58.39 MW, ≈55.85%, and ≈52.79%, respectively. The maximum exergy destruction ratios are found as 68.2% for the combustion chamber, 16.0% for the topping turbine, 5.7% for topping compressor, 4.9% air saturator. The integration of Maisotsenko's cycle-based waste heat recovery system with a comprehensive thermodynamic model, as demonstrated in this research, offers valuable insights into enhancing the efficiency, cost-effectiveness, and environmental impact of gas turbines. By presenting fundamental equations related to thermodynamic balances, this work serves as an invaluable educational resource, equipping future researchers and students with the knowledge and skills needed to advance the study of thermodynamics and sustainable energy solutions.

One hundred years of neurosciences in the arts and humanities, a bibliometric review.

BACKGROUND: Neuroscientific approaches have historically triggered changes in the conception of creativity and artistic experience, which can be revealed by noting the intersection of these fields of study in terms of variables such as global trends, methodologies, objects of study, or application of new technologies; however, these neuroscientific approaches are still often considered as disciplines detached from the arts and humanities. In this light, the question arises as to what evidence the history of neurotechnologies provides at the intersection of creativity and aesthetic experience. METHODS: We conducted a century-long bibliometric analysis of key parameters in multidisciplinary studies published in the Scopus database. Screening techniques based on the PRISMA method and advanced data analysis techniques were applied to 3612 documents metadata from the years 1922 to 2022. We made graphical representations of the results applying algorithmic and clusterization processes to keywords and authors relationships. RESULTS: From the analyses, we found a) a shift from a personality-focus quantitative analysis to a field-focus qualitative approach, considering topics such as art, perception, aesthetics and beauty; b) The locus of interest in fMRI-supported neuroanatomy has been shifting toward EEG technologies and models based on machine learning and deep learning in recent years; c) four main clusters were identified in the study approaches: humanistic, creative, neuroaesthetic and medical; d) the neuroaesthetics cluster is the most central and relevant, mediating between creativity and neuroscience; e) neuroaesthetics and neuroethics are two of the neologism that better characterizes the challenges that this convergence of studies will have in the next years. CONCLUSIONS: Through a longitudinal analysis, we evidenced the great influence that neuroscience is having on the thematic direction of the arts and humanities. The perspective presented shows how this field is being consolidated and helps to define it as a new opportunity of great potential for future researchers.